Preprints
https://doi.org/10.5194/egusphere-2023-2586
https://doi.org/10.5194/egusphere-2023-2586
20 Nov 2023
 | 20 Nov 2023

Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar

Phillip Harder, Warren Helgason, and John Pomeroy

Abstract. Despite decades of effort, there remains an inability to measure snow water equivalent (SWE) at high spatial resolutions using remote sensing. Passive gamma ray spectrometry is one of the only well-established methods to reliably remotely sense SWE, but airborne applications to date have been limited to observing km-scale areal averages over shallow snowcovers. Noting the increasing capabilities of unoccupied aerial vehicles (UAVs) and miniaturization of passive gamma ray spectrometers, this study tested the ability of a UAV-borne gamma spectrometer and concomitant UAV-borne lidar to quantify the spatial variability of SWE at high spatial resolutions. Gamma and lidar observations from a UAV were collected over two seasons from shallow, wind-blown, prairie snowpacks in Saskatchewan, Canada with validation data collected from manual snow depth and density observations. The ability of UAV-gamma to resolve the areal average and spatial variability of SWE was promising with appropriate flight characteristics. Survey flights flown at a velocity of 5 m s-1, altitude of 15 m, and line spacing of 15 m were unable to capture the average or spatial variability of SWE within the uncertainty of the reference dataset. Slower, lower, and denser flight lines at a velocity of 4 m s-1, altitude of 8 m, and line spacing of 8 m were able to successfully observe areal average SWE and its variability at spatial resolutions greater than 22.5 m. Using a combination of UAV-based gamma SWE and UAV-based lidar snow depth improved the results substantially and permitted estimation of SWE at a spatial resolution of greater than 0.25 m with a ±14.3 mm SWE error relative to manual snow survey density and UAV-lidar based depths to estimate SWE. UAV-borne gamma spectrometry to estimate SWE is a promising and novel technique that has the potential to improve the measurement of shallow prairie snowpacks, and when combined with UAV-borne lidar snow depths, can provide high resolution, high accuracy estimates of prairie SWE. Research on optimal hardware, data processing, and interpolation techniques is called for to further improve this remote sensing product and explore its application in other environments.

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Phillip Harder, Warren Helgason, and John Pomeroy

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Phillip Harder, Warren Helgason, and John Pomeroy

Data sets

UAV-borne gamma spectrometry and lidar observations of prairie snow water equivalent P. Harder, W. D. Helgason, and J. W. Pomeroy https://www.frdr-dfdr.ca/

Phillip Harder, Warren Helgason, and John Pomeroy

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Short summary
Remote sensing the amount of water in snow (SWE) at high spatial resolutions is an unresolved challenge. In this work, we tested a drone-mounted passive gamma spectrometer to quantify SWE. We found that the gamma observations could resolve the average and spatial variability of SWE down to 22.5 m resolutions. Further, by combining drone gamma SWE and lidar snow depth we could estimate SWE at sub-meter resolutions which is a new opportunity to improve the measurement of shallow snowpacks.